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Data Preparation and Overview

Setup

Standard packages

library(here)
source(here("code", "standard_libraries.R"))

Additional Packages

suppressPackageStartupMessages({
library(DropletUtils)
})

Set parameter

set.seed(123)
bpp <- BiocParallel::MulticoreParam(parallel::detectCores()-1, RNGseed=123)
path <- here::here()

Load Data

Load Cellbender output matrices

Load in the filtered feature barcode matrices from Cellbender of each sample and save them as sce object.

#Load Dataset
rawdata_folder <- paste0(path,"/data/cellbender_data_h5/")

#Get file names
filenames <- list.files(rawdata_folder ,recursive = F, full.names = F,pattern = "\\_filtered.h5$")
filepaths <- paste(rawdata_folder,filenames,sep = "")

#Load data as sce object
sce<-read10xCounts(samples = filepaths, sample.names = filenames,col.names=T,type="HDF5")

#Edit Filenames 
colData(sce)$Sample = sub("\\_w_introns.*", "", sub("cellbender_output_", "", as.character(colData(sce)$Sample)))


sce
class: SingleCellExperiment 
dim: 36601 119652 
metadata(1): Samples
assays(1): counts
rownames(36601): ENSG00000243485 ENSG00000237613 ... ENSG00000278817
  ENSG00000277196
rowData names(3): ID Symbol Type
colnames(119652): 1_AAGTACCGTCTCTCAC-1 1_GACTCTCAGGTAACTA-1 ...
  17_AGGTGTTGTGGCATCC-1 17_GCCAGCACACAAGTGG-1
colData names(2): Sample Barcode
reducedDimNames(0):
mainExpName: NULL
altExpNames(0):

Load and add Metadata

#Metadata path
metadata_folder <- paste0(path,"/data/metadata/Metadata_Master.csv")

#Load metadata
metadata_df <-read.csv(metadata_folder, header=TRUE, sep=",")
metadata_df
#Add metadata to sce
colData(sce) <- dplyr::left_join(as.data.frame(colData(sce)),
                                   metadata_df, 
                                   by= c("Sample" = "Sample"),
                                   suffix=c(".x",".y")) %>% 
      #dplyr::select(Sample, Barcode, Diagnosis, sex, Age, Joint.Location, protocol, Pathotype,Krenn total score,) %>% 
      dplyr::select(-one_of("Comments")) %>% #select all except
      DataFrame(row.names=colnames(sce))

#Set Sample names
names(colData(sce))[which(names(colData(sce))=="Sample")]="Orig.Identifier"
names(colData(sce))[which(names(colData(sce))=="ID")]="Sample"

#make row and col names unique
colnames(sce) <- paste0(sce$Sample, ".", sce$Barcode)
rownames(sce) <- paste0(rowData(sce)$ID, ".", rowData(sce)$Symbol)

Exclude Samples

#Dimensions of count matrix
dim(sce)
[1]  36601 119652

Explore dataset

Dimensions of the count matrix

#Feautures/row data
data.frame(colData(sce))
#Droplet details / row data
data.frame(rowData(sce))

Exploratory plots

Histogramm with number of cells

Show the number of cells detected in each sample or joint location before filtering

Per Sample

#Histogramm with number of cells per sample
ggplot(colData(sce), aes(x=Sample))+geom_bar()+ coord_flip()+ ggtitle("Number of cells per sample") + theme_bw()

Version Author Date
984bfbe sarloet 2024-05-02
fc17296 sarloet 2024-04-23
b1fc000 sarloet 2024-04-19
5067fa6 sarloet 2024-04-04
bc77174 sarloet 2024-04-03
d291fd3 sarloet 2024-04-02 
data.frame(as.list(table(colData(sce)$Sample)))

Per Joint Location

#Histogramm with number of cells per sample
ggplot(colData(sce), aes(x=Joint.Location))+geom_bar()+ coord_flip()+ ggtitle("Number of cells per sample") + theme_bw()

Version Author Date
984bfbe sarloet 2024-05-02
fc17296 sarloet 2024-04-23
b1fc000 sarloet 2024-04-19
5067fa6 sarloet 2024-04-04
bc77174 sarloet 2024-04-03 
data.frame(as.list(table(colData(sce)$Joint.Location)))

Plot number of genes detected per cell

This plot shows cell counts per sample / count occurrence

#Number of genes detected per cell
#Total UMI for a gene versus the number of times detected
genesPerCell <- colSums(counts(sce) > 0)
plot(density(genesPerCell), xlab="Genes per cell", main="Number of genes detected per cell")

Version Author Date
984bfbe sarloet 2024-05-02
b1fc000 sarloet 2024-04-19
5067fa6 sarloet 2024-04-04
bc77174 sarloet 2024-04-03
d291fd3 sarloet 2024-04-02

Plot transcript capture efficiency

This plot gives an idea about the sequencing depth and if the sequencing has reached saturation or not. Plotted is the total gene count across all cells (x-axis) vs Proportion of cells the gene is detected in (y-axis) where each dot represents a gene.

#transcript_capture_efficiency
#Total UMI for a gene versus the number of times detected
tmpCounts <- counts(sce)

plot(rowSums(tmpCounts),
     rowMeans(tmpCounts > 0),
     log = "x",
     xlab="total number of UMIs",
     ylab="proportion of cells expressing the gene",
     main="Total UMI for a gene vs times detected")

Version Author Date
984bfbe sarloet 2024-05-02
b1fc000 sarloet 2024-04-19
5067fa6 sarloet 2024-04-04
bc77174 sarloet 2024-04-03
d291fd3 sarloet 2024-04-02

Initial Filtering

Initial filtering to remove unexpressed genes and cells with very low number of counts. The input to scDblFinder should not include empty droplets, and it might be necessary to remove cells with a very low coverage (e.g. <200 reads) to avoid errors. Further quality filtering should be performed downstream of doublet detection.

#Initial filtering before droplet removal
dim_before_filtering <- dim(sce)

#Get only the detected Genes
#Remove Cells with very low counts of less than 150 and genes not expressed 
sce <- sce[rowSums(counts(sce)> 0) > 0, colSums(counts(sce)> 0) > 200]


dim_after_filtering <- dim(sce)

cat("NR of Cells Before Initial Filtering: ", dim_before_filtering[2],
    "\nNR of Cells After Initial Filtering: ", dim_after_filtering[2],
    "\nNR of Cells Filtered out: ", dim_before_filtering[2] - dim_after_filtering[2],
    "\nCells Filtered out: [%]", (dim_before_filtering[2] - dim_after_filtering[2])/dim_before_filtering[2]*100,
    
    "\n\nNR of Genes Before Initial Filtering: ", dim_before_filtering[1],
    "\nNR of Genes After Initial Filtering: ", dim_after_filtering[1],
    "\nNR of Genes Filtered out: ", dim_before_filtering[1] - dim_after_filtering[1],
    "\nGenes Filtered out: [%]", (dim_before_filtering[1] - dim_after_filtering[1])/dim_before_filtering[1]*100
    )
NR of Cells Before Initial Filtering:  119652 
NR of Cells After Initial Filtering:  91083 
NR of Cells Filtered out:  28569 
Cells Filtered out: [%] 23.87674 

NR of Genes Before Initial Filtering:  36601 
NR of Genes After Initial Filtering:  34594 
NR of Genes Filtered out:  2007 
Genes Filtered out: [%] 5.483457

Doublet Detection

Doublets are defined as two cells that are sequenced under the same cellular barcode, which happens if they were captured in the same droplet. The scDblFinder method combines the strengths of various doublet detection approaches, training an iterative classifier on the neighborhood of real cells and artificial doublets.

Detection

#Detection
sce <- scDblFinder::scDblFinder(sce, samples=sce$Sample, BPPARAM = bpp) 

table(sce$scDblFinder.class)

singlet doublet 
  82769    8314 
as.data.frame.matrix(table(sce$Sample,sce$scDblFinder.class))

Doublet Detection Plots

Singlet/Doublet Histogramm

Absolute comparison
#Plot singlet/doublet histogramm Absolute comparison / by sample
as.data.frame(colData(sce)) %>%
  dplyr::group_by(Sample, scDblFinder.class) %>%
  dplyr::summarise(Freq=n()) %>%
  ggplot(aes(x=Sample, y=Freq, fill=scDblFinder.class,label=Freq)) + 
    geom_bar(stat="identity") +
    labs(title="Doublet detection results",
    subtitle="By sample",x="",y="Number of Cells") +
    geom_text(size=3, position = position_stack(vjust=0.5))+
    theme(axis.text.x = element_text(angle = 45,hjust=1), axis.line = element_line(colour = "black"), panel.background = element_rect(fill = NA))

Relative comparison
# Plot singlet/doublet histogramm Relative comparison / by sample
as.data.frame(colData(sce)) %>%
  dplyr::group_by(Sample, scDblFinder.class) %>%
  dplyr::summarise(Freq=n()) %>% 
  ggplot(aes(x=Sample, y=Freq, fill=scDblFinder.class, label=Freq)) +
    geom_bar(stat="identity", position="fill") +
    labs(title="Doublet detection results",
       subtitle="By sample",
       x="Sample",
       y="Number of cells") + 
    geom_text(size=2.5, position = position_fill(vjust=0.5)) +
    theme(axis.text.x = element_text(angle = 45,hjust=1), axis.line = element_line(colour = "black"), panel.background = element_rect(fill = NA))

Singlet/Doublet Scatter

sce <- addPerCellQC(sce)

#Plot singlet/doublet qcplots
colData(sce) %>% 
    as.data.frame() %>% 
    arrange(scDblFinder.class) %>% 
    ggplot(aes(x = sce$sum, y = sce$detected )) +
      geom_point(aes(colour = scDblFinder.class)) + 
      facet_wrap(vars(Sample))+ 
      labs(title="Total number of detected genes plotted against total number of UMIs",
       x="Total counts",
       y="Detected genes") +
      theme(strip.background=element_rect(fill="white"), panel.background = element_rect(fill = NA),axis.line = element_line(colour = "black"))

Apply Doublet Removal

print(paste0("NR of Cells Before Doublet Removal ", dim(sce)[2]))
[1] "NR of Cells Before Doublet Removal 91083"
#Apply Doublet Removal
sce <- sce[ ,sce$scDblFinder.class == "singlet"]

print(paste0("NR of Cells After Doublet Removal ", dim(sce)[2]))
[1] "NR of Cells After Doublet Removal 82769"

Save the dataset

saveRDS(sce, file =paste0(path,'/output/00_sce_DataPreparation.rds'))

sessionInfo()
R version 4.3.3 (2024-02-29)
Platform: x86_64-apple-darwin20 (64-bit)
Running under: macOS Sonoma 14.4.1

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: Europe/Warsaw
tzcode source: internal

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] DropletUtils_1.22.0         gridExtra_2.3              
 [3] scran_1.30.2                scater_1.30.1              
 [5] scuttle_1.12.0              SingleCellExperiment_1.24.0
 [7] SummarizedExperiment_1.32.0 Biobase_2.62.0             
 [9] GenomicRanges_1.54.1        GenomeInfoDb_1.38.8        
[11] IRanges_2.36.0              S4Vectors_0.40.2           
[13] BiocGenerics_0.48.1         MatrixGenerics_1.14.0      
[15] matrixStats_1.3.0           dplyr_1.1.4                
[17] ggplot2_3.5.0               BiocParallel_1.36.0        
[19] here_1.0.1                  workflowr_1.7.1            

loaded via a namespace (and not attached):
  [1] rstudioapi_0.16.0         jsonlite_1.8.8           
  [3] magrittr_2.0.3            ggbeeswarm_0.7.2         
  [5] farver_2.1.1              rmarkdown_2.26           
  [7] fs_1.6.3                  BiocIO_1.12.0            
  [9] zlibbioc_1.48.2           vctrs_0.6.5              
 [11] Rsamtools_2.18.0          DelayedMatrixStats_1.24.0
 [13] RCurl_1.98-1.14           htmltools_0.5.8.1        
 [15] S4Arrays_1.2.1            BiocNeighbors_1.20.2     
 [17] xgboost_1.7.7.1           Rhdf5lib_1.24.2          
 [19] SparseArray_1.2.4         rhdf5_2.46.1             
 [21] sass_0.4.9                bslib_0.7.0              
 [23] cachem_1.0.8              GenomicAlignments_1.38.2 
 [25] whisker_0.4.1             igraph_2.0.3             
 [27] lifecycle_1.0.4           pkgconfig_2.0.3          
 [29] rsvd_1.0.5                Matrix_1.6-5             
 [31] R6_2.5.1                  fastmap_1.1.1            
 [33] GenomeInfoDbData_1.2.11   digest_0.6.35            
 [35] colorspace_2.1-0          ps_1.7.6                 
 [37] rprojroot_2.0.4           dqrng_0.3.2              
 [39] irlba_2.3.5.1             beachmat_2.18.1          
 [41] labeling_0.4.3            fansi_1.0.6              
 [43] httr_1.4.7                abind_1.4-5              
 [45] compiler_4.3.3            withr_3.0.0              
 [47] viridis_0.6.5             highr_0.10               
 [49] HDF5Array_1.30.1          R.utils_2.12.3           
 [51] MASS_7.3-60.0.1           DelayedArray_0.28.0      
 [53] rjson_0.2.21              bluster_1.12.0           
 [55] tools_4.3.3               vipor_0.4.7              
 [57] beeswarm_0.4.0            httpuv_1.6.15            
 [59] R.oo_1.26.0               glue_1.7.0               
 [61] restfulr_0.0.15           callr_3.7.6              
 [63] rhdf5filters_1.14.1       promises_1.3.0           
 [65] grid_4.3.3                getPass_0.2-4            
 [67] cluster_2.1.6             generics_0.1.3           
 [69] gtable_0.3.4              R.methodsS3_1.8.2        
 [71] data.table_1.15.4         BiocSingular_1.18.0      
 [73] ScaledMatrix_1.10.0       metapod_1.10.1           
 [75] utf8_1.2.4                XVector_0.42.0           
 [77] ggrepel_0.9.5             pillar_1.9.0             
 [79] stringr_1.5.1             limma_3.58.1             
 [81] later_1.3.2               lattice_0.22-5           
 [83] rtracklayer_1.62.0        tidyselect_1.2.1         
 [85] locfit_1.5-9.9            Biostrings_2.70.3        
 [87] knitr_1.45                git2r_0.33.0             
 [89] edgeR_4.0.16              xfun_0.43                
 [91] statmod_1.5.0             stringi_1.8.3            
 [93] yaml_2.3.8                evaluate_0.23            
 [95] codetools_0.2-19          tibble_3.2.1             
 [97] cli_3.6.2                 munsell_0.5.1            
 [99] processx_3.8.4            jquerylib_0.1.4          
[101] Rcpp_1.0.12               XML_3.99-0.16.1          
[103] parallel_4.3.3            sparseMatrixStats_1.14.0 
[105] bitops_1.0-7              viridisLite_0.4.2        
[107] scales_1.3.0              scDblFinder_1.16.0       
[109] crayon_1.5.2              rlang_1.1.3